Huge number of Internet of Things (IoT) devices are available to promote health care management and wellness. It is undoubted that IoT Healthcare solutions can provide remote monitoring to support patients suffering from various diseases and disorders. But, a gamut of expensive sensor devices, sophisticated, periodic setup, maintenance and calibration as well as up-to-date training are required for such purpose to come to fruition. In order to promote widespread usage and affordability, such costly and extensive intricacies do not work positively towards the ubiquity and success of mobile and preventable health care, specifically in developing countries. As a result, deriving various physiological parameters of a person in a noninvasive and affordable manner is a significant task and challenge. Pulsating signals have gained high importance for the purpose of derivation of various physiological parameters of a person. As a result, extracting the various time series features of pulsating signals like photoplethysmogram (PPG) signal and then co-relating them with physiological parameters is a necessity to create noninvasive and affordable healthcare analytics applications.
The inventors here have recognized certain technical problems with such conventional systems, as explained below. In the majority of conventional systems, a pulsating signal like photoplethysmogram (PPG) signal has lot of noise and the analytics to be performed are mostly run on low power battery operated devices like mobile phones. This causes errors and increased resource usage in conventional systems that are being used for deriving various physiological parameters of a person in a noninvasive and affordable manner. Thereby, deriving physiological parameters from pulsating signals with reduced error while requiring low computational power is still considered to be one of the biggest challenges of the technical domain.